Automated Dried Fish Classification Using MobileNetV2 and Transfer Learning

India, the second largest fish producer globally, contributes significantly to food security, nutrition, and economic development. Dried fish is a vital component of the fisheries value chain, especially in South Asia, yet current classification methods are manual, inconsistent, and labor-intensive. This study aims to automate dried fish classification using MobileNetV2 through transfer learning, enabling real-time, lightweight deployment on edge devices. We trained and evaluated the model across four diverse publicly available datasets using single, bulk, head, and tail image modalities. Our experiments demonstrated high accuracy (up to 100%) and strong generalization across datasets. The proposed model offers a practical, scalable, and efficient solution to modernize dried fish processing and enhance productivity and traceability in fisheries.

@article{Pardeshi2025,
  title     = {Automated Dried Fish Classification Using MobileNetV2 and Transfer Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Rajmohan Pardeshi and Rajermani Thinakaran and Sanjay Kharat},
  doi       = {10.14569/IJACSA.2025.0160780},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160780}
}